The performance of a cellular network is affected by a collection of factors such as the data and voice traffic load, the RF coverage, the level of inter-cell interference, the location of users, and hardware failures. In many cases, the performance of a few wireless cells within a cellular network may appear abnormal, and mobile users that are served by these cells will suffer from poor user experience. A poor user experience will give rise to customer dissatisfaction. As a remedy, operators often need to detect the abnormal behaviors and then take actions to fix the problems. Traditionally, operators rely on network experts to analyze the behavior of a particular cell to identify the root causes. Traditional approaches of root cause analysis for wireless cellular networks are generally based on a correlation study or rely heavily on engineering knowledge. Such approaches are often heuristic in nature and it is in general difficult to quantify its accuracy. These approaches are also very time-consuming. It may take a few hours if not days to identify the root causes for the performance degradation.